code: def render_plot(
      result: view_types.EvalResult,
      slicing_spec: Optional[
          Union[slicer.SingleSliceSpec, config_pb2.SlicingSpec]
      ] = None,
      output_name: Optional[str] = None,
      class_id: Optional[int] = None,
      top_k: Optional[int] = None,
      k: Optional[int] = None,
      label: Optional[str] = None,
  ) -> Optional[
      visualization.PlotViewer
  ]:  # pytype: disable=invalid-annotation
      """Renders the plot view as widget.
  
      Args:
        result: An tfma.EvalResult.
        slicing_spec: The tfma.SlicingSpec to identify the slice. Show overall if
          unset.
        output_name: A string representing the output name.
        class_id: A number representing the class id if multi class.
        top_k: The k used to compute prediction in the top k position.
        k: The k used to compute prediciton at the kth position.
        label: A partial label used to match a set of plots in the results.
  
      Returns:
        A PlotViewer object if in Jupyter notebook; None if in Colab.
      """
      if slicing_spec and isinstance(
          slicing_spec, config_pb2.SlicingSpec
      ):
          slicing_spec = slicer.SingleSliceSpec(spec=slicing_spec)
      slice_spec_to_use = (
          slicing_spec if slicing_spec else slicer.SingleSliceSpec()
      )
      data, cfg = util.get_plot_data_and_config(
          result.plots,
          slice_spec_to_use,
          output_name,
          class_id,
          top_k,
          k,
          label,
      )
      return visualization.render_plot(data, cfg)
  
file_path: model-analysis/tensorflow_model_analysis/view/widget_view.py
imports: from typing import Callable, Dict, Optional, Union
  from tensorflow_model_analysis import constants
  import tensorflow_model_analysis.notebook.visualization
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from tensorflow_model_analysis.view import util
  from tensorflow_model_analysis.view import view_types
label_desc: Use image node_id 3 for calling a global function with example usage: render_plot(result, slicing_spec, output_name, class_id, top_k, k, label) and returns: visualization